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2. Combining paper cooperative network and topic model for expert topic analysis and extraction.

Author

Gao, Shengxiang, Li, Xian, Yu, Zhengtao, Qin, Yu, and Zhang, Yang

Subjects
*PAPER, *EXTRACTION techniques, *GIBBS sampling, *PROBABILISTIC databases, *NEURAL computers
Abstract

Paper cooperation network embodies expert topic similarity in an extent, thus, a novel method is proposed for expert topic analysis and extraction by combining paper cooperation network and topic model. In the method, we extract each paper’ author information and construct an expert cooperation network. At the same time, by means of LDA model, a probabilistic topic model is also built to analyze papers’ latent topics. Then, by making full use of the feature that adjacent nodes in the expert cooperation network share similar themes distribution, we makes a constraint on expert topic distribution in Gibbs sampling process of solving the probabilistic topic model. Experimental results on NIPS dataset show that the proposed method can effectively extract expert topics, and the expert paper cooperation network plays a very good supporting role on the extracting task. [ABSTRACT FROM AUTHOR]

Published
2017
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12. Combining paper cooperative network and topic model for expert topic analysis and extraction

Author

Xian Li, Zhengtao Yu, Yang Zhang, Yu Qin, and Shengxiang Gao

Subjects
Topic model, business.industry, Process (engineering), Computer science, Cognitive Neuroscience, Probabilistic logic, 02 engineering and technology, Legal expert system, Machine learning, computer.software_genre, Computer Science Applications, Constraint (information theory), symbols.namesake, Artificial Intelligence, 020204 information systems, Similarity (psychology), 0202 electrical engineering, electronic engineering, information engineering, symbols, Feature (machine learning), 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Gibbs sampling
Abstract

Paper cooperation network embodies expert topic similarity in an extent, thus, a novel method is proposed for expert topic analysis and extraction by combining paper cooperation network and topic model. In the method, we extract each paper’ author information and construct an expert cooperation network. At the same time, by means of LDA model, a probabilistic topic model is also built to analyze papers’ latent topics. Then, by making full use of the feature that adjacent nodes in the expert cooperation network share similar themes distribution, we makes a constraint on expert topic distribution in Gibbs sampling process of solving the probabilistic topic model. Experimental results on NIPS dataset show that the proposed method can effectively extract expert topics, and the expert paper cooperation network plays a very good supporting role on the extracting task.

Published
2017